ANOVA

An abbreviation for an analysis of variance, which is a parametric procedure for determining whether significant differences in an experiemnt that contains two or more conditions. ANOVA is a test of inferential statistics.

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Clinical vs. Statistical Significance

A result is statistically significant if it is unlikely to have occurred by chance.

A result is clinically significant if it results in a noticeable and appreciable difference in the client’s everyday functioning.

Statistical significance is determined by a mathematical/statistical procedure. 

Clinical significance is determined by the amount of change the client sees in their life.

Statistical significance does not necessarily equate to clinical significance.

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Construct Validity

In research design, construct validity is the degree to which a test or study measures the construct that it is purported to measure. 

There are two further aspects to construct validity. 

Convergent validity is how well a certain measure of a construct correlates with other well-established measures of that construct.

Divergent validity is how the measure of a construct does not correlate with other measure.

In order to have high construct validity, a test should correlate highly with other measures of the same construct, and not correlate highly with measures of other constructs. 

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Content Validity

In research design, content validity is the degree to which a test or study includes all of the facets of the construct it is attempting to measure. 

It cannot be measured empirically but is rather assessed through logical analysis.

Content validity is related to face validity, but is not the same**.

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Correlation vs. Causation

In the context of research, correlation refers to the existence of a relationship between two variables, while causation refers to a cause-effect relationship between two variables.

Correlation is not the same thing as causation, however, a correlation between two variables is necessary before it can be established that one causes a change in the other. 

Only experimental studies can establish that one variable causes a change in another variable.

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Correlational Research

A type of research design in which the researchers attempt to determine if there is a relationship, or correlation, between two variables, and if so, what the strength and direction of the relationship is.

Correlational research involves a statistical procedure which results in a correlation coefficient which ranges from 1.0 to -1.0, depending on the strength and direction of the relationship between the two variables. 

Correlational research does not have the ability to establish causation.

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Cross-sectional Design

This is a form of between-subjects research design in which groups of individuals of different ages are compared to each other. 

Typically, several dependent variables are measured, and the study itself rarely takes more than a few months to complete.

Cross-sectional designs are advantageous because they take less time and therefore less money, but are disadvantageous because they give little information about the stability of the dependent variables and the change in them over time.

Ex: Jean opted to perform a cross-sectional study comparing groups of 6 year olds, 10 year olds, and 14 year olds on intelligence, peer relations, and relationships with parents. Her study was preliminary and, as a result, she was unable to receive funding for a longer study, making this type of design more practical.

Dependent t-test

A statistical procedure that is appropriate for significance testing when the scores meet the requirements of a parametric test, the design involves matched groups or repeated measures, and there are only two conditions of the independent variable.

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Descriptive vs. Inferential

Descriptive statistics are those which are used to concisely describe a data set.

Inferential statistics are those which use a smaller representative sample to draw conclusions about a larger group.

Descriptive statistics can only be used to describe the sample that they are conducted on.

Inferential statistics can be used to make generalizations about a larger population from a small sample.

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Double-blind Study

A type of experimental design in which both the participants and the researchers are unaware of who is in the experimental condition and who is in the placebo condition.

This is in contrast to a single-blind, where only the participants are unaware.

Double-blind studies eliminate the possibility that the researcher may somehow communicate (knowingly or unknowingly) to a participant which condition they are in, thereby contaminating the results.

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Ecological Validity

In the context of research, this is the extent to which an experimental situation approximates the real-life situation which is being studied.

Researchers have called for making experiments more ecologically valid in hopes that they would generalize better to the real world.

Although external and ecological validity are related, they are independent and not the same concept.

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Simply a way of quantifying the size of the difference between two groups.

It is easy to calculate, readily understood and can be applied to any measured outcome.

It is particularly valuable for quantifying the effectiveness of a particular intervention, relative to some comparison. It allows us to move beyond the simplistic, 'Does it work or not?' to the far more sophisticated, 'How well does it work in a range of contexts?' 

Experimental Research

A form of research in which one variable, the independent variable, is manipulated in order to see what effect it will have on another variable, the dependent variable.

Researchers will try to control any other variables (confounds) that may affect the dependent variable, in order to establish that if a change occurred it was caused by the independent variable.

Experimental research is the only kind of research which can establish causation.

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Hypothesis

In the field of research, a hypothesis is a formally stated prediction that can be tested for its accuracy.

Hypotheses are essential parts of the scientific method.

In a study, hypotheses help to focus the research and bring it to a meaningful conclusion.

Many hypotheses go into the making of a psychological theory.

Without hypotheses, it is impossible to test theories.

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Independent t-test

A statistical procedure used for significance testing that is appropriate when the scores meet the requirements of a parametric test, the design involves independent samples, and there are only two conditions of the independent variable. 

Independent t-test is important in determining significance.

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Internal Consistency

In the context of research, this is a type of reliability (also known as split-half reliability) in which the items on a measure are divided in half and the two halves are scored separately. 

The scores between the two halves are then correlated.

This type of reliability tends to underestimate the reliability for a measure, and the Spearman-Brown formula was developed in order to correct this.

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Internal Validity

This is the extent to which the observed relationship between variables in a study reflects the actual relationship between the variables. 

A study that is internally valid is free from flaws in its internal structure and therefore may establish a causal relationship.

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Interrater Reliability

In research design, this is a type of reliability that measures the agreement level between independent raters. 

It is used with measures that are less objective and more subjective.

This type of reliability is used to account for human error in the form of distractibility or misinterpretation. 

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Measures of Central Tendency

In statistics, these help to summarize the main features of a data st and identify the score around which most scores fall. 

Three primary measures are used: the mean, the mode, and the median. 

The mean is the arithmetic average of all scores within a data set; the mode is the most frequently occurring score; the median is the point that separate the distribution into two equal halves.

The median and the mode are not as affected by outliers as the mean.

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Measures of Variability

In statistics, these are measures of how scores in a distribution deviate or vary around the central tendency. 

Three primary measures: range, variance, and standard deviation.

The range is obtained from taking the two most extreme scores and subtracting the lowest from the highest.

The variance is the average squared deviation around the mean, and must be squared because the sum of the deviation would equal zero. 

The standard deviation is the square root of the variance, and is highly useful in describing variability. 

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Nominal/Ordinal/Interval/Ratio Measurements

In statistics, these are four types of scales of measurement.

Nominal scales are used for categorical groupings, and have none of the three properties that distinguish scales. 

Ordinal scales are used for rankings of individuals or variables, and have the property of magnitude.

Interval scales have magnitude and have equal intervals between any two observations, but do not have the property of absolute zero.

Ratio scales have all three properties of scales: magnitude, equal intervals, and absolute zero.

The type of scales used dictates what statistical procedures may be run on a data set.

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Normal Curve

In the context of statistics, this describes the frequency of a distribution in which most occurrences take place in the middle and taper off to either side of the mean.

It is symmetrical, with one half of scores falling above the mean and one half below.

The mean, median, and mode are all the same.

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Probability

In statistics, probability is a mathematical statement indicating the likelihood that particular event will occur when particular population is randomly sampled, symbolized by (p). 

The higher the p value is, the more likely that the phenomenon or event happened by chance.

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Parametric vs. Nonparametric Statistical Analyses

In statistics, parametric statistical analyses are procedures that require certain assumptions about the parameters of the raw score population represented by the sample data. 

Usually used with scores most appropriately described by the mean.

Nonparametric statistical analyses involved inferential procedures that do not require stringent assumptions about the parameters of the raw score population represented by the sample data

Usually used with scores most appropriately described by the median or mode. 

Parametric analyses are preferred because they have greater statistical power and are more likely to detect statistical significance.

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Quasi-experimental Research

A type of research design in which the independent variable has already been selected and cannot be manipulated.

Participants are assigned to a group based on some inherent characteristic. 

Types of quasi-experimental independent variables include age, sex, & race, which have been selected by nature and cannot be manipulated; being sexually abused or raped is also a quasi-experimental independent variable, as it would not be ethical to assign that condition to anyone.

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Random Sampling

A method of selecting participants from the population for a given study in which all members of the population being studied have an equal chance of being chosen or sampled.

It is important because it is used to reduce the potential for bias in experiments.

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Regression

This is a statistical technique in which one variable is used to predict or estimate the score of another variable.

Regressions use rare score data, in contrast to correlations which use standardized scores.

The goal is to find a regression line that maximizes prediction accuracy and minimizes error.

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Sample vs. Population

The population is the large group of all scores that would be obtained if the behavior of every individual of interest in a particular situation could be measured.

A sample is a relatively small subset of the population that is selected to represent the population in inferential statistics, as it would be impossible to study the entire population.

It is important that the sample is representative of the population being studied.

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Scientific Methodology

This is an empirically based way of conducting research and studying human behavior. 

It is largely a four step process: first, researchers conceptualize a process or problem to be studied. Next they collect relevant data through research. In the third step, they analyze the data they have collected. Lastly, they draw conclusions based on their analyses.

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Standard Error of Estimate

In the context of statistics, this is a standard deviation in a regression which indicates the amount that the actual Y scores differ from the predicted Y scores. 

Standard error of the estimate is also known as a standard error of the residuals. 

It is a measure of the accuracy of an estimate.

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Standard Error of Measurement

In the context of testing, this is an estimate of how much an individual’s score on a measurement would be expected to change upon re-resting with the same or a similar test.

The SEM can be used to estimate the interval in which an individual’s true score would be expected to fall.

It also reminds the evaluator that the test score is just an estimate and is not exact.

SEM has an inverse relationship with the reliability coefficient.

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Standard Error of the Difference (2 sample t-test)

In the context of statistical analysis, this is the estimated standard deviation of the differences between the means of independent samples in a two-sample experiment.

In other words, this is the error between groups.

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Standard Error of the Mean (single sample z-test)

In the context of statistical analysis, this is the standard deviation of the sampling distribution of the mean.

This is a type of standard deviation used when the population mean and standard deviation are known.

It is used to stimate how much the sample mean deviates from the population mean.

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Standard Error of the Mean, Estimated (single sample t-test)

In the context of statistical analysis, this is an unknown standard deviation.

One can make an estimate of the standard deviation of the sampling population of interest.

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Standardization Sample

This is a representative cross-section of a population that will be taking a given test, who are administered the test under standard conditions.

Their results are used to establish a normal score range on norm-referenced tests.

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Statistical Significance

In the context of statistics and research, results are statistically significant when they are unlikely to have occurred simply by chance.

Testing for statistical significance used a “t distribution” and a criterion of significance (i.e., .05, .01) is selected. 

When a test is statistically significant, the null hypothesis is rejected.

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Type I and Type II Error

In the context of statistical analysis, these are two types of errors seen in research. 

A Type I error occurs when researchers incorrectly conclude that the independent variable(s) had an effect on the dependent variable(s).

A Type II error occurs when the researchers incorrectly conclude that the independent variable(s) had no effect on the dependent variable(s) and accept the null hypothesis.

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